Benchmarking carbon emissions efficiency in Chinese cities: A comparative study based on high-resolution gridded data

Cai, Bofeng; Guo, Huanxiu; Ma, Zipeng; Wang, Zhixuan; Dhakal, Shobhakar; Cao, Liguo

doi:10.1016/j.apenergy.2019.03.146

Cited by 66 publications

(26 citation statements)

References 51 publications

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“…These results are consistent with previous studies of the spatial distribution of carbon emissions in Chinese cities. The total quantity of emissions exhibited a gradual decreasing trend when moving from eastern to western regions [ 53 – 55 ].…”

Section: Resultsmentioning

confidence: 99%

Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt

2021

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China became the country with the largest global carbon emissions in 2007. Cities are regional population and economic centers and are the main sources of carbon emissions. However, factors influencing carbon emissions from cities can vary with geographic location and the development history of the cities, rendering it difficult to explicitly quantify the influence of individual factors on carbon emissions. In this study, random forest (RF) machine learning algorithms were applied to analyze the relationships between factors and carbon emissions in cities using real-world data from Chinese cities. Seventy-three cities in three urban agglomerations within the Yangtze River Economic Belt were evaluated with respect to urban carbon emissions using data from regional energy balance tables for the years 2000, 2007, 2012, and 2017. The RF algorithm was then used to select 16 prototypical cities based on 10 influencing factors that affect urban carbon emissions while considering five primary factors: population, industry, technology levels, consumption, and openness to the outside world. Subsequently, 18 consecutive years of data from 2000 to 2017 were used to construct RFs to investigate the temporal predictability of carbon emission variation in the 16 cities based on regional differences. Results indicated that the RF approach is a practical tool to study the connection between various influencing factors and carbon emissions in the Yangtze River Economic Belt from different perspectives. Furthermore, regional differences among the primary carbon emission influencing factors for each city were clearly observed and were related to urban population characteristics, urbanization level, industrial structures, and degree of openness to the outside world. These factors variably affected different cities, but the results indicate that regional emission reductions have achieved positive results, with overall simulation trends shifting from underestimation to overestimation of emissions.

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Section: Resultsmentioning

confidence: 99%

Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt

2021

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“…During the analysis of input-output efficiency, some scholars have adopted the stochastic frontier analysis (SFA) to quantitatively explore the influence of efficiency differences of decision-making units (DMUs). These were based on economic theories to get more accurate results with a set method model (Dong et al 2013;Cai et al 2019;Jin and Kim 2019;Sun and Huang 2020). However, not only does the SFA model need to be set in the form of specific functions, but the assumptions must also be relatively strict.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on carbon emission efficiency in the Chinese construction industry based on a three-stage DEA-Tobit model

Zhang

Cheng

2021

Environ Sci Pollut Res

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The traditional data envelopment analysis (DEA) model usually ignores the influence of external environmental factors and random interference. This can easily lead to deviations in efficiency estimates. In order to solve this problem, a three-stage DEA model was used to better reflect the carbon emission efficiency of Chinese construction industry (CEECI) (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) from the perspective of non-management factors. The internal influencing factors of CEECI are analyzed by the Tobit model, which provides a more accurate basis for formulating policies. It is found that the CEECI is significantly affected by the GDP, the level of industrialization, the degree of opening-up, technological innovation and energy structure. After excluding environmental factors and random interference, the average CEECI increased by 16%. The resulting calculations were noteworthy in three aspects. First, there are significant regional differences in the CEECI. Both the multipolarization phenomenon of CEECI and regional differences also reduced gradually over time. Second, the CEECI can be decomposed into pure carbon emission efficiency (PCEE) and scale efficiency (SE), which is mainly caused by SE. Excluding external environmental factors and random interference will have a specific impact on the CEECI. All the 30 provinces are divided into four categories to analyze the reasons and solutions of the differences in the CEECI in provinces. Third, many factors had inhibitory effects on the CEECI, PCEE and SE; these included energy structure optimization, labor force number, total power of construct ion equipment and construction intensity in the construction industry. Nevertheless, the development level of the construction industry did have a significant positive effect.

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“…In this paper, we first estimate the full sample panel data of China, and observe how the estimation results differ from time series data analysis. Then, in order to explore how FD affects CE in different regions with different characteristics, we refer to Yan et al [31], Pan et al [32], Zhen et al [33] and Cai et al [34] and divide four sub-panels from two dimensions of CE and FD. We calculate the average CE and FD of each province in China from 1997 to 2016.…”

Section: Full-sample Panel Data and Sub-sample Panel Datamentioning

confidence: 99%

The Impact of Financial Development on Carbon Emission: Evidence from China

Guo

2020

Sustainability

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This paper studies the impact of financial development on carbon emissions in China from 1997 to 2016. First, this paper uses the entropy method to construct a synthetical index to measure the financial development. Meanwhile, a two-dimensional panel framework is introduced to group provinces in the panel analysis. The estimation results of the time series autoregressive distributed lag model show that for China as a whole, there is a weak carbon emissions reduction effect of financial development, whether it is a long-term effect or a short-term effect. The estimation results of the panel autoregressive distributed lag model also support that an increase in financial development suppresses carbon emissions. Although financial development inhibits carbon emissions both in the short run and in the long run, the absolute value of the long-term coefficient of financial development is significantly greater than that of the short-term coefficient.

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Benchmarking carbon emissions efficiency in Chinese cities: A comparative study based on high-resolution gridded data

Cited by 66 publications

References 51 publications

Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt

Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt

Research on carbon emission efficiency in the Chinese construction industry based on a three-stage DEA-Tobit model

The Impact of Financial Development on Carbon Emission: Evidence from China

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